






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=224		,		_UpperCAmelCase=1000		,		_UpperCAmelCase=[3, 3, 6, 4]		,		_UpperCAmelCase=[48, 56, 112, 220]		,		)      ->			Optional[int]:
     UpperCamelCase_						=  parent
     UpperCamelCase_						=  batch_size
     UpperCamelCase_						=  num_channels
     UpperCamelCase_						=  is_training
     UpperCamelCase_						=  use_labels
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  num_labels
     UpperCamelCase_						=  image_size
     UpperCamelCase_						=  layer_depths
     UpperCamelCase_						=  embed_dims




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     UpperCamelCase_						=  None
     if self.use_labels:
        UpperCamelCase_						=  ids_tensor([self.batch_size]		,		self.num_labels			)

     UpperCamelCase_						=  self.get_config()

     return config, pixel_values, labels




  def  _UpperCAmelCase  (  self			)      ->			str:
     return SwiftFormerConfig(
         depths=self.layer_depths		,		embed_dims=self.embed_dims		,		mlp_ratio=4		,		downsamples=[True, True, True, True]		,		hidden_act='gelu'		,		num_labels=self.num_labels		,		down_patch_size=3		,		down_stride=2		,		down_pad=1		,		drop_rate=0.0		,		drop_path_rate=0.0		,		use_layer_scale=_UpperCAmelCase		,		layer_scale_init_value=1e-5		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			int:
     UpperCamelCase_						=  SwiftFormerModel(config=_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()
     UpperCamelCase_						=  model(_UpperCAmelCase			)
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.embed_dims[-1], 7, 7)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  self.num_labels
     UpperCamelCase_						=  SwiftFormerForImageClassification(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()
     UpperCamelCase_						=  model(_UpperCAmelCase		,		labels=_UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)			)

     UpperCamelCase_						=  SwiftFormerForImageClassification(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()

     UpperCamelCase_						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
     UpperCamelCase_						=  model(_UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     ((UpperCamelCase_)		,   (UpperCamelCase_)		,   (UpperCamelCase_))						=  self.prepare_config_and_inputs()
     UpperCamelCase_						=  {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  A_						       = (
      {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  A_						       = False
  A_						       = False
  A_						       = False
  A_						       = False
  A_						       = False




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  SwiftFormerModelTester(self			)
     UpperCamelCase_						=  ConfigTester(
         self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37		,		num_attention_heads=12		,		num_hidden_layers=12		,		)




  def  _UpperCAmelCase  (  self			)      ->			str:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='SwiftFormer does not use inputs_embeds'			)
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     pass




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        UpperCamelCase_						=  model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase		,		nn.Linear			)			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        UpperCamelCase_						=  inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCamelCase_						=  [*signature.parameters.keys()]

        UpperCamelCase_						=  ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_						=  SwiftFormerModel.from_pretrained(_UpperCAmelCase			)
        self.assertIsNotNone(_UpperCAmelCase			)




  @unittest.skip(reason='SwiftFormer does not output attentions'			)
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     pass




  def  _UpperCAmelCase  (  self			)      ->			int:
     def check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        model.to(_UpperCAmelCase			)
        model.eval()

        with torch.no_grad():
           UpperCamelCase_						=  model(**self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase			)			)

        UpperCamelCase_						=  outputs.hidden_states

        UpperCamelCase_						=  8
        self.assertEqual(len(_UpperCAmelCase			)		,		_UpperCAmelCase			)  # TODO

        # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
        # with the width and height being successively divided by 2, after every 2 blocks
        for i in range(len(_UpperCAmelCase			)			):
           self.assertEqual(
               hidden_states[i].shape		,		torch.Size(
                   [
                       self.model_tester.batch_size,
                       self.model_tester.embed_dims[i // 2],
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                   ]			)		,		)

     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  True
        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCamelCase_						=  True

        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     def _config_zero_init(_UpperCAmelCase			):
        UpperCamelCase_						=  copy.deepcopy(_UpperCAmelCase			)
        for key in configs_no_init.__dict__.keys():
           if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
              setattr(_UpperCAmelCase		,		_UpperCAmelCase		,		1e-10			)
           if isinstance(getattr(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)		,		_UpperCAmelCase			):
              UpperCamelCase_						=  _config_zero_init(getattr(_UpperCAmelCase		,		_UpperCAmelCase			)			)
              setattr(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
        return configs_no_init

     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_						=  _config_zero_init(_UpperCAmelCase			)
     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(config=_UpperCAmelCase			)
        for name, param in model.named_parameters():
           if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9) / 1e9).round().item()		,		[0.0, 1.0]		,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,		)




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     pass






def      _snake_case     ():
   UpperCamelCase_						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_torch
@require_vision
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'			) if is_vision_available() else None




  @slow
  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'			).to(_UpperCAmelCase			)

     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(images=_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)

     # forward pass
     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

     # verify the logits
     UpperCamelCase_						=  torch.Size((1, 1000)			)
     self.assertEqual(outputs.logits.shape		,		_UpperCAmelCase			)

     UpperCamelCase_						=  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		_UpperCAmelCase		,		atol=1e-4			)			)













'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


